

__snake_case				:      int   		=			[
    """Audio""",
    """Array2D""",
    """Array3D""",
    """Array4D""",
    """Array5D""",
    """ClassLabel""",
    """Features""",
    """Sequence""",
    """Value""",
    """Image""",
    """Translation""",
    """TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=4  ,      )     -> Tuple:
									a_						:						Tuple 							= parent
									a_						:						Optional[Any] 							= batch_size
									a_						:						int 							= seq_length
									a_						:						List[str] 							= is_training
									a_						:						str 							= use_attention_mask
									a_						:						Optional[int] 							= use_token_type_ids
									a_						:						Optional[int] 							= use_labels
									a_						:						int 							= vocab_size
									a_						:						Union[str, Any] 							= hidden_size
									a_						:						Optional[int] 							= num_hidden_layers
									a_						:						Optional[int] 							= num_attention_heads
									a_						:						Tuple 							= intermediate_size
									a_						:						int 							= hidden_act
									a_						:						Any 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						List[str] 							= max_position_embeddings
									a_						:						Optional[int] 							= type_vocab_size
									a_						:						List[str] 							= type_sequence_label_size
									a_						:						Optional[Any] 							= initializer_range
									a_						:						Optional[int] 							= num_choices





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						int 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						Dict 							= None
									if self.use_attention_mask:
												a_						:						List[str] 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						List[str] 							= None
									if self.use_token_type_ids:
												a_						:						Dict 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Any 							= RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      )

									return config, input_ids, token_type_ids, attention_mask





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						Any 							= config_and_inputs
									a_						:						List[Any] 							= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						str 							= config_and_inputs

									a_						:						str 							= True
									a_						:						Any 							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									a_						:						Tuple 							= ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2    )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				List[str]       					=			True

						_A    :				int       					=			(
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= FlaxRobertaPreLayerNormModelTester(self    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> int:
									for model_class_name in self.all_model_classes:
												a_						:						Union[str, Any] 							= model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )
												a_						:						Union[str, Any] 							= model(np.ones((1, 1)    )    )
												self.assertIsNotNone(_lowercase    )







@require_flax
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@slow
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						Union[str, Any] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						Tuple 							= model(_lowercase    )[0]
									a_						:						Tuple 							= [1, 11, 50_265]
									self.assertEqual(list(output.shape    )  ,      _lowercase    )
									# compare the actual values for a slice.
									a_						:						Tuple 							= np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						List[str] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						str 							= model(_lowercase    )[0]
									# compare the actual values for a slice.
									a_						:						Union[str, Any] 							= np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )


